By Liang Wu, Yuanchun Zhou, Fei Tan, Fenglei Yang (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed lawsuits of the seventh foreign convention on complex facts Mining and purposes, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers offered including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers hide a variety of issues offering unique learn findings in info mining, spanning purposes, algorithms, software program and platforms, and utilized disciplines.
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Extra resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
Each will hhave different cost based on . Then, it uses these cost sensitive classifiers on nnew data (test data) to obtain estimate e class distributions and / of test data frrom each model. AVG / is calculated from equation 5. AVG pr/nr ∑N pr/nr N (5) Now, CDE-EM-AVG-N caalculates DMR again from all the models that have bbeen built. It substitutes / by AVG / then it builds new cost sensittive classifier from the original data d (train data) using this cost. Flow chart of CDE-EM-AVG-N is shown in Figure 2.
The detailed accuracy results for the Adult data set are shown in Figure 3 and Table 2. 00% Test data possitive rate (%) Fig. 3. Experiment 1: Accuracy Performance on Adult Dataset Handling Concept Drift via Ensemble and Class Distribution Estimation Technique 21 Table 2. 00% Test data possitive rate (%) Fig. 4. Experiment 2: Accuracy Performance on Magic Gamma Dataset 22 N. Limsetto and K. Waiyamai Table 3. 66% In the following, we report some interesting results obtained from the different methods in case of normal situation where the class distribution is obtained from real-world data.
00% Test data possitive rate (%) Fig. 4. Experiment 2: Accuracy Performance on Magic Gamma Dataset 22 N. Limsetto and K. Waiyamai Table 3. 66% In the following, we report some interesting results obtained from the different methods in case of normal situation where the class distribution is obtained from real-world data. We notice that increasing iteration number of previous method does not necessary increase the accuracy of CDE-iterate-n, as shown in Table 3. Thus, we stop at the iteration number 2 (Iterate2).
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